{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "CS-DDQN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lZPwAxel7x9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e7c035d5-633d-45dd-ad31-033525aa6e64"
      },
      "source": [
        "#Imports and gym creation\n",
        "import gym\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import deque\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "#from keras.models import Sequential\n",
        "#from keras.layers import Dense\n",
        "#from keras.optimizers import Adam\n",
        "import random\n",
        "\n",
        "#Create Gym\n",
        "from gym import wrappers\n",
        "envCartPole = gym.make('CartPole-v1')\n",
        "envCartPole.seed(50)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[50]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VGDdEytl7yK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPISODES = 500\n",
        "TRAIN_END = 0"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtS6cFn3l7yS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def discount_rate(): #Gamma\n",
        "    return 0.95\n",
        "\n",
        "def learning_rate(): #Alpha\n",
        "    return 0.001\n",
        "\n",
        "def batch_size():\n",
        "    return 24"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDZtfUW0l7yn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DoubleDeepQNetwork():\n",
        "    def __init__(self, states, actions, alpha, gamma, epsilon,epsilon_min, epsilon_decay):\n",
        "        self.nS = states\n",
        "        self.nA = actions\n",
        "        self.memory = deque([], maxlen=2500)\n",
        "        self.alpha = alpha\n",
        "        self.gamma = gamma\n",
        "        #Explore/Exploit\n",
        "        self.epsilon = epsilon\n",
        "        self.epsilon_min = epsilon_min\n",
        "        self.epsilon_decay = epsilon_decay\n",
        "        self.model = self.build_model()\n",
        "        self.model_target = self.build_model() #Second (target) neural network\n",
        "        self.update_target_from_model() #Update weights\n",
        "        self.loss = []\n",
        "        \n",
        "    def build_model(self):\n",
        "        model = keras.Sequential() #linear stack of layers https://keras.io/models/sequential/\n",
        "        model.add(keras.layers.Dense(24, input_dim=self.nS, activation='relu')) #[Input] -> Layer 1\n",
        "        #   Dense: Densely connected layer https://keras.io/layers/core/\n",
        "        #   24: Number of neurons\n",
        "        #   input_dim: Number of input variables\n",
        "        #   activation: Rectified Linear Unit (relu) ranges >= 0\n",
        "        model.add(keras.layers.Dense(24, activation='relu')) #Layer 2 -> 3\n",
        "        model.add(keras.layers.Dense(self.nA, activation='linear')) #Layer 3 -> [output]\n",
        "        #   Size has to match the output (different actions)\n",
        "        #   Linear activation on the last layer\n",
        "        model.compile(loss='mean_squared_error', #Loss function: Mean Squared Error\n",
        "                      optimizer=keras.optimizers.Adam(lr=self.alpha)) #Optimaizer: Adam (Feel free to check other options)\n",
        "        return model\n",
        "\n",
        "    def update_target_from_model(self):\n",
        "        #Update the target model from the base model\n",
        "        self.model_target.set_weights( self.model.get_weights() )\n",
        "\n",
        "    def action(self, state):\n",
        "        if np.random.rand() <= self.epsilon:\n",
        "            return random.randrange(self.nA) #Explore\n",
        "        action_vals = self.model.predict(state) #Exploit: Use the NN to predict the correct action from this state\n",
        "        return np.argmax(action_vals[0])\n",
        "\n",
        "    def test_action(self, state): #Exploit\n",
        "        action_vals = self.model.predict(state)\n",
        "        return np.argmax(action_vals[0])\n",
        "\n",
        "    def store(self, state, action, reward, nstate, done):\n",
        "        #Store the experience in memory\n",
        "        self.memory.append( (state, action, reward, nstate, done) )\n",
        "\n",
        "    def experience_replay(self, batch_size):\n",
        "        #Execute the experience replay\n",
        "        minibatch = random.sample( self.memory, batch_size ) #Randomly sample from memory\n",
        "\n",
        "        #Convert to numpy for speed by vectorization\n",
        "        x = []\n",
        "        y = []\n",
        "        np_array = np.array(minibatch)\n",
        "        st = np.zeros((0,self.nS)) #States\n",
        "        nst = np.zeros( (0,self.nS) )#Next States\n",
        "        for i in range(len(np_array)): #Creating the state and next state np arrays\n",
        "            st = np.append( st, np_array[i,0], axis=0)\n",
        "            nst = np.append( nst, np_array[i,3], axis=0)\n",
        "        st_predict = self.model.predict(st) #Here is the speedup! I can predict on the ENTIRE batch\n",
        "        nst_predict = self.model.predict(nst)\n",
        "        nst_predict_target = self.model_target.predict(nst) #Predict from the TARGET\n",
        "        index = 0\n",
        "        for state, action, reward, nstate, done in minibatch:\n",
        "            x.append(state)\n",
        "            #Predict from state\n",
        "            nst_action_predict_target = nst_predict_target[index]\n",
        "            nst_action_predict_model = nst_predict[index]\n",
        "            if done == True: #Terminal: Just assign reward much like {* (not done) - QB[state][action]}\n",
        "                target = reward\n",
        "            else:   #Non terminal\n",
        "                target = reward + self.gamma * nst_action_predict_target[np.argmax(nst_action_predict_model)] #Using Q to get T is Double DQN\n",
        "            target_f = st_predict[index]\n",
        "            target_f[action] = target\n",
        "            y.append(target_f)\n",
        "            index += 1\n",
        "        #Reshape for Keras Fit\n",
        "        x_reshape = np.array(x).reshape(batch_size,self.nS)\n",
        "        y_reshape = np.array(y)\n",
        "        epoch_count = 1\n",
        "        hist = self.model.fit(x_reshape, y_reshape, epochs=epoch_count, verbose=0)\n",
        "        #Graph Losses\n",
        "        for i in range(epoch_count):\n",
        "            self.loss.append( hist.history['loss'][i] )\n",
        "        #Decay Epsilon\n",
        "        if self.epsilon > self.epsilon_min:\n",
        "            self.epsilon *= self.epsilon_decay"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-KiANrlYl7ys",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create the agents\n",
        "nS = envCartPole.observation_space.shape[0] #This is only 4\n",
        "nA = envCartPole.action_space.n #Actions\n",
        "dqn1 = DoubleDeepQNetwork(nS, nA, learning_rate(), discount_rate(), 1, 0.005, 0.995 )\n",
        "dqn2 = DoubleDeepQNetwork(nS, nA, learning_rate(), discount_rate(), 1, 0.005, 0.995 )\n",
        "\n",
        "\n",
        "batch_size = batch_size()"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XdxLERIl7yy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "af2872f8-c965-4426-c257-4412f23354f9"
      },
      "source": [
        "#Trainin\n",
        "rewards = [] #Store rewards for graphing\n",
        "epsilons = [] # Store the Explore/Exploit\n",
        "TEST_Episodes = 0\n",
        "for e in range(EPISODES):\n",
        "    state = envCartPole.reset()\n",
        "    state = np.reshape(state, [1, nS]) # Resize to store in memory to pass to .predict\n",
        "    tot_rewards = 0\n",
        "    prev_action = 0\n",
        "    for time in range(210): #200 is when you \"solve\" the game. This can continue forever as far as I know\n",
        "        action1 = dqn1.action(state)\n",
        "        action2 = dqn2.action(state)\n",
        "\n",
        "        if ((action1==0 and action2==0) or (action1==1 and action2==1)):\n",
        "          action=prev_action\n",
        "        elif (action1==1 and action2==0):\n",
        "          action=0\n",
        "        else:\n",
        "          action=1  \n",
        "        prev_action = action\n",
        "        nstate, reward, done, _ = envCartPole.step(action)\n",
        "        nstate = np.reshape(nstate, [1, nS])\n",
        "        tot_rewards += reward\n",
        "        dqn1.store(state, action1, reward, nstate, done) # Resize to store in memory to pass to .predict\n",
        "        dqn2.store(state, action2, reward, nstate, done) # Resize to store in memory to pass to .predict\n",
        "        state = nstate\n",
        "        #done: CartPole fell. \n",
        "        #time == 209: CartPole stayed upright\n",
        "        if done or time == 209:\n",
        "            rewards.append(tot_rewards)\n",
        "            epsilons.append(dqn1.epsilon)\n",
        "            print(\"episode: {}/{}, score: {}, e: {}\"\n",
        "                  .format(e, EPISODES, tot_rewards, dqn1.epsilon))\n",
        "            break\n",
        "        #Experience Replay\n",
        "        if len(dqn1.memory) > batch_size:\n",
        "            dqn1.experience_replay(batch_size)\n",
        "        if len(dqn2.memory) > batch_size:    \n",
        "            dqn2.experience_replay(batch_size)\n",
        "    #Update the weights after each episode (You can configure this for x steps as well\n",
        "    dqn1.update_target_from_model()\n",
        "    dqn2.update_target_from_model()\n",
        "    #If our current NN passes we are done\n",
        "    #I am going to use the last 5 runs\n",
        "    if len(rewards) > 5 and np.average(rewards[-5:]) > 195:\n",
        "        #Set the rest of the EPISODES for testing\n",
        "        TEST_Episodes = EPISODES - e\n",
        "        TRAIN_END = e\n",
        "        break"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "episode: 0/500, score: 17.0, e: 1\n",
            "episode: 1/500, score: 11.0, e: 0.985074875\n",
            "episode: 2/500, score: 11.0, e: 0.9369146928798039\n",
            "episode: 3/500, score: 26.0, e: 0.8265651079747222\n",
            "episode: 4/500, score: 16.0, e: 0.7666961448653229\n",
            "episode: 5/500, score: 16.0, e: 0.7111635524897149\n",
            "episode: 6/500, score: 11.0, e: 0.6763948591909945\n",
            "episode: 7/500, score: 14.0, e: 0.6337242817644086\n",
            "episode: 8/500, score: 14.0, e: 0.5937455908197752\n",
            "episode: 9/500, score: 12.0, e: 0.5618938591163328\n",
            "episode: 10/500, score: 17.0, e: 0.5185893309484582\n",
            "episode: 11/500, score: 14.0, e: 0.4858739637363176\n",
            "episode: 12/500, score: 12.0, e: 0.4598090507939749\n",
            "episode: 13/500, score: 21.0, e: 0.4159480862733536\n",
            "episode: 14/500, score: 8.0, e: 0.4016064652978155\n",
            "episode: 15/500, score: 10.0, e: 0.38389143477919885\n",
            "episode: 16/500, score: 21.0, e: 0.3472722151889232\n",
            "episode: 17/500, score: 9.0, e: 0.33362200135903064\n",
            "episode: 18/500, score: 20.0, e: 0.3033145315372582\n",
            "episode: 19/500, score: 9.0, e: 0.2913921604631864\n",
            "episode: 20/500, score: 10.0, e: 0.27853872940185365\n",
            "episode: 21/500, score: 8.0, e: 0.2689348941735696\n",
            "episode: 22/500, score: 21.0, e: 0.24328132378095624\n",
            "episode: 23/500, score: 15.0, e: 0.22679417751985861\n",
            "episode: 24/500, score: 28.0, e: 0.19808659230739353\n",
            "episode: 25/500, score: 13.0, e: 0.1865228530605915\n",
            "episode: 26/500, score: 18.0, e: 0.1712870076899825\n",
            "episode: 27/500, score: 28.0, e: 0.14960551470448571\n",
            "episode: 28/500, score: 48.0, e: 0.11820406108847166\n",
            "episode: 29/500, score: 43.0, e: 0.09576389217857617\n",
            "episode: 30/500, score: 30.0, e: 0.08280777787605056\n",
            "episode: 31/500, score: 37.0, e: 0.0691356479495614\n",
            "episode: 32/500, score: 48.0, e: 0.05462441922520914\n",
            "episode: 33/500, score: 37.0, e: 0.045605554379880814\n",
            "episode: 34/500, score: 87.0, e: 0.029634846598205186\n",
            "episode: 35/500, score: 45.0, e: 0.023769400938086327\n",
            "episode: 36/500, score: 39.0, e: 0.01964695745288379\n",
            "episode: 37/500, score: 74.0, e: 0.013626370684745774\n",
            "episode: 38/500, score: 75.0, e: 0.0094034706692354\n",
            "episode: 39/500, score: 61.0, e: 0.0069610222032810655\n",
            "episode: 40/500, score: 57.0, e: 0.005257333661306802\n",
            "episode: 41/500, score: 128.0, e: 0.004975301787985069\n",
            "episode: 42/500, score: 51.0, e: 0.004975301787985069\n",
            "episode: 43/500, score: 63.0, e: 0.004975301787985069\n",
            "episode: 44/500, score: 83.0, e: 0.004975301787985069\n",
            "episode: 45/500, score: 87.0, e: 0.004975301787985069\n",
            "episode: 46/500, score: 120.0, e: 0.004975301787985069\n",
            "episode: 47/500, score: 49.0, e: 0.004975301787985069\n",
            "episode: 48/500, score: 52.0, e: 0.004975301787985069\n",
            "episode: 49/500, score: 35.0, e: 0.004975301787985069\n",
            "episode: 50/500, score: 106.0, e: 0.004975301787985069\n",
            "episode: 51/500, score: 124.0, e: 0.004975301787985069\n",
            "episode: 52/500, score: 95.0, e: 0.004975301787985069\n",
            "episode: 53/500, score: 51.0, e: 0.004975301787985069\n",
            "episode: 54/500, score: 71.0, e: 0.004975301787985069\n",
            "episode: 55/500, score: 74.0, e: 0.004975301787985069\n",
            "episode: 56/500, score: 83.0, e: 0.004975301787985069\n",
            "episode: 57/500, score: 79.0, e: 0.004975301787985069\n",
            "episode: 58/500, score: 81.0, e: 0.004975301787985069\n",
            "episode: 59/500, score: 72.0, e: 0.004975301787985069\n",
            "episode: 60/500, score: 115.0, e: 0.004975301787985069\n",
            "episode: 61/500, score: 116.0, e: 0.004975301787985069\n",
            "episode: 62/500, score: 53.0, e: 0.004975301787985069\n",
            "episode: 63/500, score: 60.0, e: 0.004975301787985069\n",
            "episode: 64/500, score: 80.0, e: 0.004975301787985069\n",
            "episode: 65/500, score: 113.0, e: 0.004975301787985069\n",
            "episode: 66/500, score: 95.0, e: 0.004975301787985069\n",
            "episode: 67/500, score: 79.0, e: 0.004975301787985069\n",
            "episode: 68/500, score: 101.0, e: 0.004975301787985069\n",
            "episode: 69/500, score: 83.0, e: 0.004975301787985069\n",
            "episode: 70/500, score: 102.0, e: 0.004975301787985069\n",
            "episode: 71/500, score: 100.0, e: 0.004975301787985069\n",
            "episode: 72/500, score: 210.0, e: 0.004975301787985069\n",
            "episode: 73/500, score: 210.0, e: 0.004975301787985069\n",
            "episode: 74/500, score: 210.0, e: 0.004975301787985069\n",
            "episode: 75/500, score: 210.0, e: 0.004975301787985069\n",
            "episode: 76/500, score: 210.0, e: 0.004975301787985069\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOJOCs1zl7y4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "101826d5-125f-47fd-ab3d-7d04646aeed6"
      },
      "source": [
        "#Testing\n",
        "print('Training complete. Testing started...')\n",
        "TEST_Episodes=100\n",
        "#TEST Time\n",
        "#   In this section we ALWAYS use exploit don't train any more\n",
        "for e_test in range(TEST_Episodes):\n",
        "    state = envCartPole.reset()\n",
        "    state = np.reshape(state, [1, nS])\n",
        "    tot_rewards = 0\n",
        "    prev_action = 0\n",
        "    for t_test in range(210):\n",
        "\n",
        "        action1 = dqn1.action(state)\n",
        "        action2 = dqn2.action(state)\n",
        "\n",
        "        if ((action1==0 and action2==0) or (action1==1 and action2==1)):\n",
        "          action=prev_action\n",
        "        elif (action1==1 and action2==0):\n",
        "          action=0\n",
        "        else:\n",
        "          action=1  \n",
        "        prev_action = action\n",
        "        #action = dqn.test_action(state)\n",
        "        nstate, reward, done, _ = envCartPole.step(action)\n",
        "        nstate = np.reshape( nstate, [1, nS])\n",
        "        tot_rewards += reward\n",
        "        #DON'T STORE ANYTHING DURING TESTING\n",
        "        state = nstate\n",
        "        #done: CartPole fell. \n",
        "        #t_test == 209: CartPole stayed upright\n",
        "        if done or t_test == 209: \n",
        "            rewards.append(tot_rewards)\n",
        "            epsilons.append(0) #We are doing full exploit\n",
        "            print(\"episode: {}/{}, score: {}, e: {}\"\n",
        "                  .format(e_test, TEST_Episodes, tot_rewards, 0))\n",
        "            break;"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training complete. Testing started...\n",
            "episode: 0/100, score: 210.0, e: 0\n",
            "episode: 1/100, score: 210.0, e: 0\n",
            "episode: 2/100, score: 210.0, e: 0\n",
            "episode: 3/100, score: 210.0, e: 0\n",
            "episode: 4/100, score: 210.0, e: 0\n",
            "episode: 5/100, score: 210.0, e: 0\n",
            "episode: 6/100, score: 210.0, e: 0\n",
            "episode: 7/100, score: 210.0, e: 0\n",
            "episode: 8/100, score: 210.0, e: 0\n",
            "episode: 9/100, score: 210.0, e: 0\n",
            "episode: 10/100, score: 210.0, e: 0\n",
            "episode: 11/100, score: 210.0, e: 0\n",
            "episode: 12/100, score: 210.0, e: 0\n",
            "episode: 13/100, score: 210.0, e: 0\n",
            "episode: 14/100, score: 210.0, e: 0\n",
            "episode: 15/100, score: 210.0, e: 0\n",
            "episode: 16/100, score: 210.0, e: 0\n",
            "episode: 17/100, score: 210.0, e: 0\n",
            "episode: 18/100, score: 210.0, e: 0\n",
            "episode: 19/100, score: 210.0, e: 0\n",
            "episode: 20/100, score: 210.0, e: 0\n",
            "episode: 21/100, score: 210.0, e: 0\n",
            "episode: 22/100, score: 210.0, e: 0\n",
            "episode: 23/100, score: 210.0, e: 0\n",
            "episode: 24/100, score: 210.0, e: 0\n",
            "episode: 25/100, score: 210.0, e: 0\n",
            "episode: 26/100, score: 210.0, e: 0\n",
            "episode: 27/100, score: 210.0, e: 0\n",
            "episode: 28/100, score: 210.0, e: 0\n",
            "episode: 29/100, score: 210.0, e: 0\n",
            "episode: 30/100, score: 210.0, e: 0\n",
            "episode: 31/100, score: 210.0, e: 0\n",
            "episode: 32/100, score: 210.0, e: 0\n",
            "episode: 33/100, score: 210.0, e: 0\n",
            "episode: 34/100, score: 210.0, e: 0\n",
            "episode: 35/100, score: 210.0, e: 0\n",
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            "episode: 94/100, score: 210.0, e: 0\n",
            "episode: 95/100, score: 210.0, e: 0\n",
            "episode: 96/100, score: 210.0, e: 0\n",
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            "episode: 98/100, score: 210.0, e: 0\n",
            "episode: 99/100, score: 210.0, e: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9RA93KWl7y_",
        "colab_type": "text"
      },
      "source": [
        "**Results**  \n",
        "Here is a graph of the results. If everything was done correctly you should see the rewards over the red line.  \n",
        "\n",
        "Black: This is the 100 episode rolling average  \n",
        "Red: This is the \"solved\" line at 195  \n",
        "Blue: This is the reward for each episode  \n",
        "Green: This is the value of epsilon scaled by 200  \n",
        "Yellow: This is where the tests started."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Bg7A5Jo0dBf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "bc4e979f-c57e-4558-86be-e61261444308"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "np.save('/content/drive/My Drive/db/dqn/ddqn/rewards_cs',rewards)\n",
        "np.save('/content/drive/My Drive/db/dqn/ddqn/TRAIN_END_cs',TRAIN_END)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_0FrVCWl7zA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "263dc047-1507-4910-a322-8094e323706f"
      },
      "source": [
        "#Plotting\n",
        "rolling_average = np.convolve(rewards, np.ones(100)/100)\n",
        "\n",
        "plt.plot(rewards)\n",
        "plt.plot(rolling_average, color='black')\n",
        "plt.axhline(y=195, color='r', linestyle='-') #Solved Line\n",
        "#Scale Epsilon (0.001 - 1.0) to match reward (0 - 200) range\n",
        "eps_graph = [200*x for x in epsilons]\n",
        "plt.plot(eps_graph, color='g', linestyle='-')\n",
        "#Plot the line where TESTING begins\n",
        "plt.axvline(x=TRAIN_END, color='y', linestyle='-')\n",
        "plt.xlim( (0,EPISODES) )\n",
        "plt.ylim( (0,220) )\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJgJr8Wr5xqp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "e24bac5f-797b-4f6a-c7a9-e89748ca504c"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure(figsize=(10,5),dpi=500)\n",
        "\n",
        "r_old=np.load('/content/drive/My Drive/db/dqn/ddqn/rewards.npy')\n",
        "r_new=np.load('/content/drive/My Drive/db/dqn/ddqn/rewards_cs.npy')\n",
        "TE_old=np.load('/content/drive/My Drive/db/dqn/ddqn/TRAIN_END.npy')\n",
        "TE_new=np.load('/content/drive/My Drive/db/dqn/ddqn/TRAIN_END_cs.npy')\n",
        "\n",
        "#rolling_average1 = np.convolve(r_old, np.ones(100)/100)\n",
        "#rolling_average2 = np.convolve(r_new, np.ones(100)/100)\n",
        "\n",
        "plt.plot(r_old, color= 'lightblue',label='DDQN')\n",
        "plt.plot(r_new, color='steelblue',label='CS-DDQN')\n",
        "#plt.plot(rolling_average1, color='grey')\n",
        "#plt.plot(rolling_average1, color='black')\n",
        "plt.axhline(y=195, color='r', linestyle='-') #Solved Line\n",
        "#Scale Epsilon (0.001 - 1.0) to match reward (0 - 200) range\n",
        "#eps_graph = [200*x for x in epsilons]\n",
        "#plt.plot(eps_graph, color='g', linestyle='-')\n",
        "#Plot the line where TESTING begins\n",
        "plt.axvline(x=TE_old, color='sandybrown', linestyle='-')\n",
        "plt.axvline(x=TE_new, color='saddlebrown', linestyle='-')\n",
        "plt.xlim( (0,200) )\n",
        "plt.ylim( (0,220) )\n",
        "plt.ylabel('Rewards')\n",
        "plt.title('Episodes')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 5000x2500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xcsVIVl0V_V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}